# Disable wannings
import os
import warnings
warnings.simplefilter(action = 'ignore', category=FutureWarning)
warnings.filterwarnings('ignore')
def ignore_warn(*args, **kwargs):
    pass

#Import libraries
import os
import glob
import errno
from datetime import datetime, date
import pandas as pd
import numpy as np
from collections import Counter

import imaplib
import base64
import email

import random
import matplotlib.pyplot as plt

# libraries
import numpy as np
import matplotlib.pyplot as plt
 
# set width of bar
barWidth = 0.25
 
# set height of bar
bars1 = [12, 30, 1, 8, 22]
bars2 = [28, 6, 16, 5, 10]
bars3 = [29, 3, 24, 25, 17]
 
# Set position of bar on X axis
r1 = np.arange(len(bars1))
r2 = [x + barWidth for x in r1]
r3 = [x + barWidth for x in r2]
 
# Make the plot
plt.bar(r1, bars1, color='#7f6d5f', width=barWidth, edgecolor='white', label='var1')
plt.bar(r2, bars2, color='#557f2d', width=barWidth, edgecolor='white', label='var2')
plt.bar(r3, bars3, color='#2d7f5e', width=barWidth, edgecolor='white', label='var3')
 
# Add xticks on the middle of the group bars
plt.xlabel('group', fontweight='bold')
plt.xticks([r + barWidth for r in range(len(bars1))], ['A', 'B', 'C', 'D', 'E'])
 
# Create legend & Show graphic
plt.legend()
plt.show()

# library
import matplotlib.pyplot as plt
 
# Create bars
barWidth = 0.9
bars1 = [3, 3, 1]
bars2 = [4, 2, 3]
bars3 = [4, 6, 7, 10, 4, 4]
bars4 = bars1 + bars2 + bars3
 
# The X position of bars
r1 = [1,5,9]
r2 = [2,6,10]
r3 = [3,4,7,8,11,12]
r4 = r1 + r2 + r3
 
# Create barplot
plt.bar(r1, bars1, width = barWidth, color = (0.3,0.1,0.4,0.6), label='Alone')
plt.bar(r2, bars2, width = barWidth, color = (0.3,0.5,0.4,0.6), label='With Himself')
plt.bar(r3, bars3, width = barWidth, color = (0.3,0.9,0.4,0.6), label='With other genotype')
# Note: the barplot could be created easily. See the barplot section for other examples.
 
# Create legend
plt.legend()
 
# Text below each barplot with a rotation at 90°
plt.xticks([r + barWidth for r in range(len(r4))], ['DD', 'with himself', 'with DC', 'with Silur', 'DC', 'with himself', 'with DD', 'with Silur', 'Silur', 'with himself', 'with DD', 'with DC'], rotation=90)
 
# Create labels
label = ['n = 6', 'n = 25', 'n = 13', 'n = 36', 'n = 30', 'n = 11', 'n = 16', 'n = 37', 'n = 14', 'n = 4', 'n = 31', 'n = 34']
 
# Text on the top of each barplot
for i in range(len(r4)):
    plt.text(x = r4[i]-0.5 , y = bars4[i]+0.1, s = label[i], size = 6)
 

 # Adjust the margins
plt.subplots_adjust(bottom= 0.2, top = 0.98)
 
# Show graphic
plt.show()

# libraries
import numpy as np
import matplotlib.pyplot as plt
 
# Choose the height of the bars
height = [3, 12, 5, 18, 45]
 
# Choose the names of the bars
bars = ('group1', 'group2', 'group3', 'group4', 'group5')
y_pos = np.arange(len(bars))
 
# Create bars
plt.bar(y_pos, height)
 
# Create names on the x-axis
plt.xticks(y_pos, bars, color='orange')
plt.yticks(color='orange')
 
# Show graphic
plt.show()

# Libraries
import pandas as pd
from matplotlib import pyplot as plt
from scipy.cluster.hierarchy import dendrogram, linkage
import numpy as np
 
# Data set
url = 'https://python-graph-gallery.com/wp-content/uploads/mtcars.csv'
df = pd.read_csv(url)
df = df.set_index('model')
del df.index.name
 
# Calculate the distance between each sample
Z = linkage(df, 'ward')
 
# Make the dendro
dendrogram(Z, labels=df.index, leaf_rotation=0, orientation="left", color_threshold=240, above_threshold_color='grey')
 
# Create a color palette with 3 color for the 3 cyl possibilities
my_palette = plt.cm.get_cmap("Accent", 3)
 
# transforme the 'cyl' column in a categorical variable. It will allow to put one color on each level.
df['cyl']=pd.Categorical(df['cyl'])
my_color=df['cyl'].cat.codes
 
# Apply the right color to each label
ax = plt.gca()
xlbls = ax.get_ymajorticklabels()
num=-1
for lbl in xlbls:
    num+=1
    val=my_color[num]
    
lbl.set_color(my_palette(val))

# library & dataset
import seaborn as sns
df = df_M
 
# Custom the inside plot: options are: “scatter” | “reg” | “resid” | “kde” | “hex”
#sns.jointplot(x=df["Oxygen"], y=df["Salinity_SBE45"], kind='scatter')
#sns.jointplot(x=df["Oxygen"], y=df["Salinity_SBE45"], kind='hex')
#sns.jointplot(x=df["Oxygen"], y=df["Salinity_SBE45"], kind='kde')

g = sns.jointplot("Oxygen", "Salinity_SBE45", data=df_M, kind="reg")

plt.savefig('books_read.png')
g

<seaborn.axisgrid.JointGrid at 0xc4e4400>

# Then you can pass arguments to each type:
sns.jointplot(x=df["sepal_length"], y=df["sepal_width"], kind='scatter', s=200, color='m', edgecolor="skyblue", linewidth=2)
 
# Custom the color
sns.set(style="white", color_codes=True)
sns.jointplot(x=df["sepal_length"], y=df["sepal_width"], kind='kde', color="skyblue")

<seaborn.axisgrid.JointGrid at 0x9d58390>

# libraries
import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import kde
 
# create data
x = np.random.normal(size=500)
y = x * 3 + np.random.normal(size=500)
 
# Evaluate a gaussian kde on a regular grid of nbins x nbins over data extents
nbins=300
k = kde.gaussian_kde([x,y])
xi, yi = np.mgrid[x.min():x.max():nbins*1j, y.min():y.max():nbins*1j]
zi = k(np.vstack([xi.flatten(), yi.flatten()]))
 
# Make the plot
plt.pcolormesh(xi, yi, zi.reshape(xi.shape))
plt.show()
 
# Change color palette
plt.pcolormesh(xi, yi, zi.reshape(xi.shape), cmap=plt.cm.Greens_r)
plt.show()

# library
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
 
# Get the data (csv file is hosted on the web)
url = 'https://python-graph-gallery.com/wp-content/uploads/volcano.csv'
data = pd.read_csv(url)
 
# Transform it to a long format
df=data.unstack().reset_index()
df.columns=["X","Y","Z"]
 
# And transform the old column name in something numeric
df['X']=pd.Categorical(df['X'])
df['X']=df['X'].cat.codes
 
# Make the plot
fig = plt.figure()
ax = fig.gca(projection='3d')
ax.plot_trisurf(df['Y'], df['X'], df['Z'], cmap=plt.cm.viridis, linewidth=0.2)
plt.show()
 
# to Add a color bar which maps values to colors.
surf=ax.plot_trisurf(df['Y'], df['X'], df['Z'], cmap=plt.cm.viridis, linewidth=0.2)
fig.colorbar( surf, shrink=0.5, aspect=5)
plt.show()
 
# Rotate it
ax.view_init(30, 45)
plt.show()
 
# Other palette
ax.plot_trisurf(df['Y'], df['X'], df['Z'], cmap=plt.cm.jet, linewidth=0.01)
plt.show()

# library
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
 
# Get the data (csv file is hosted on the web)
url = 'https://python-graph-gallery.com/wp-content/uploads/volcano.csv'
data = pd.read_csv(url)
 
# Transform it to a long format
df=data.unstack().reset_index()
df.columns=["X","Y","Z"]
 
# And transform the old column name in something numeric
df['X']=pd.Categorical(df['X'])
df['X']=df['X'].cat.codes
 
# We are going to do 20 plots, for 20 different angles
for angle in range(70,210,2):
 
    # Make the plot
    fig = plt.figure()
    ax = fig.gca(projection='3d')
    ax.plot_trisurf(df['Y'], df['X'], df['Z'], cmap=plt.cm.viridis, linewidth=0.2)

    # Set the angle of the camera
    ax.view_init(30,angle)
 
# Save it
filename='D:/Python/Logiciels'+str(angle)+'.png'
plt.savefig(filename, dpi=96)
plt.gca()

<matplotlib.axes._subplots.Axes3DSubplot at 0x19e104e0>

fig = plt.figure()

ax = fig.add_subplot(111)

ax.plot([1, 2, 3, 4], [10, 20, 25, 30], color='lightblue', linewidth=3)

ax.scatter([0.3, 3.8, 1.2, 2.5], [11, 25, 9, 26], color='darkgreen', marker='^')

ax.set_xlim(0.5, 4.5)

plt.show()

convert -delay 80 Gapminder*.png animated_gapminder.gif

plt.plot([1, 2, 3, 4], [10, 20, 25, 30], color='lightblue', linewidth=3)

plt.scatter([0.3, 3.8, 1.2, 2.5], [11, 25, 9, 26], color='darkgreen', marker='^')

plt.xlim(0.5, 4.5)

plt.show()

# Import the necessary packages and modules
import matplotlib.pyplot as plt
import numpy as np

# Create a Figure
fig = plt.figure()

# Set up Axes
ax = fig.add_subplot(111)

# Scatter the data
ax.scatter(np.linspace(0, 1, 5), np.linspace(0, 5, 5))

# Show the plot
plt.show()

# Import `pyplot` from `matplotlib`
import matplotlib.pyplot as plt

# Initialize the plot
fig = plt.figure(figsize=(20,10))
ax1 = fig.add_subplot(121)
ax2 = fig.add_subplot(122)

# or replace the three lines of code above by the following line: 
#fig, (ax1, ax2) = plt.subplots(1,2, figsize=(20,10))

# Plot the data
ax1.bar([1,2,3],[3,4,5])
ax2.barh([0.5,1,2.5],[0,1,2])

# Show the plot
plt.show()

# Import the necessary packages and modules
import matplotlib.pyplot as plt
import numpy as np

# Prepare the data
x = np.linspace(0, 10, 100)

# Plot the data
plt.plot(x, x, label='linear')

# Add a legend
plt.legend()

# Show the plot
plt.show()

# Import the necessary packages and modules
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np

# Uncomment following line to see the effect 
#mpl.rcParams['lines.linewidth'] = 5

# Prepare the data
x = np.linspace(0, 10, 100)

# Plot the data
plt.plot(x, x, label='linear')

# Add a legend
plt.legend()

# Show the plot
plt.show()